Assisted Cellular Networks Research Articles

Two-Stage Resource Allocation in Reconfigurable Intelligent Surface Assisted Hybrid Networks via Multi-player Bandits

This paper considers a resource allocation problem where several Internet-of-Things (IoT) devices send data to a base station (BS) with or without the help of the reconfigurable intelligent surface (RIS) assisted cellular network. The objective is to maximize the sum rate of all IoT devices by finding the optimal RIS and spreading factor (SF) for each device. Since these IoT devices lack prior information of the RISs or the channel state information (CSI), a distributed resource allocation framework with low complexity and learning features is required to achieve this goal. Therefore, we model this problem as a two-stage multi-player multi-armed bandit (MPMAB) framework to learn the optimal RIS and SF sequentially. Then, we put forth an exploration and exploitation boosting (E2Boost) algorithm to solve this two-stage MPMAB problem by combining the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> -greedy algorithm, Thompson sampling (TS) algorithm, and non-cooperation game method. We derive an upper regret bound for the proposed algorithm, i.e., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(\log ^{1+\delta }_{2} T)$ </tex-math></inline-formula> , increasing logarithmically with the time horizon <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> . Numerical results show that the E2Boost algorithm has the best performance among the existing methods and exhibits a fast convergence rate. More importantly, the proposed algorithm is not sensitive to the number of combinations of the RISs and SFs thanks to the two-stage allocation mechanism, which can benefit the high-density networks.

Deep Reinforcement Learning-Based Resource Allocation in Cooperative UAV-Assisted Wireless Networks

We consider the downlink of an unmanned aerial vehicle (UAV) assisted cellular network consisting of multiple cooperative UAVs, whose operations are coordinated by a central ground controller using wireless fronthaul links, to serve multiple ground user equipments (UEs). A problem of jointly designing UAVs’ positions, transmit beamforming, as well as UAV-UE association is formulated in the form of mixed integer nonlinear programming (MINLP) to maximize the sum UEs’ achievable rate subject to limited fronthaul capacity constraints. Solving the considered problem is hard owing to its non-convexity and the unavailability of channel state information (CSI) due to the movement of UAVs. To tackle these effects, we propose a novel algorithm comprising of two distinguishing features: (i) exploiting a deep Q-learning approach to tackle the issue of CSI unavailability for determining UAVs’ positions, (ii) developing a difference of convex algorithm (DCA) to efficiently solve for the UAV’s transmit beamforming and UAV-UE association. The proposed algorithm recursively solves the problem of interest until convergence, where each recursion executes two steps. In the first step, the deep Q-learning (DQL) algorithm allows UAVs to learn the overall network state and account for the joint movement of all UAVs to adapt their locations. In the second step, given the determined UAVs’ positions from the DQL algorithm, the DCA iteratively solves a convex approximate subproblem of the original non-convex MINLP problem with the updated parameters, where the problem’s variables are transmit beamforming and UAV-UE association. Numerical results show that our design outperforms the existing algorithms in terms of algorithmic convergence and network performance with a gain of up to 70%.

On Cooperative Achievable Rates of UAV Assisted Cellular Networks

In this paper, we study an UAV base station (UBS) assisted cellular network that consists of a single ground macro base station (MBS), multiple UBSs, and multiple ground terminals. We assume that the MBS transmits to the UBSs and the terminals in the licensed band while the UBSs use a separate unlicensed band (e.g., Wi-Fi) to transmit to the terminals. For the utilization of the UBSs, we propose a cooperative decode-forward (DF) protocol in which multiple UBSs assist the terminals simultaneously while maintaining a predetermined interference level on the coexisting unlicensed band users. For our network setup, we formulate a joint optimization problem for minimizing the aggregate gap between the target rates and the throughputs of terminals by optimizing over the 3D positions of the UBSs and the resources (power, time, bandwidth) of the network. To solve the optimization problem, we propose an efficient nested structured algorithm based on particle swarm optimization and convex optimization methods. Extensive numerical evaluations of the proposed algorithm is performed considering various aspects to demonstrate the performance of our algorithm and the gain for utilizing UBSs.

Energy Efficiency for MISO-OFDMA-Based User-Relay Assisted Cellular Networks

The concept of improving energy efficiency (EE) without sacrificing the service quality has become important nowadays. The combination of orthogonal frequency-division multiple-access (OFDMA) multiantenna transmission technology and relaying is one of the key technologies to deliver the promise of reliable and high data rate coverage in the most cost-effective manner. In this article, EE is studied for the downlink multiple-input single-output OFDMA-based user-relay assisted cellular networks. EE maximization is formulated for the decode and forward relaying scheme with the consideration of both transmit and circuit power consumption as well as the data rate requirements for the mobile users. The quality of service constrained EE maximization, which is defined for multicarrier, multiuser, multirelay, and multiantenna networks, is a nonconvex and combinatorial problem so it is hard to tackle. To solve this difficult problem, a radio resource management algorithm that solves the subcarrier allocation, mode selection, and power allocation separately is proposed. The efficiency of the proposed algorithm is demonstrated by numerical results for different system parameters.

Spectrum allocation and performance analysis for backhauling of UAV assisted cellular network

To reinforce the coverage and QoS (quality of service) of on-ground cellular communication system, unmanned aerial vehicles which are carrying small cells are deployed in some emergency and disaster areas. Although ASCs (aerial small cells) can provide a higher probability of LoS (line-of-sight) transmission, the wireless backhaul link will bring extra interference to the whole system if not well designed. Therefore, in this paper, we study the backhaul scheme for UAV-assist cellular network. We first analyze the interference environments of UAV-assist cellular network considering the IBOG (In-Band to On-Ground), OBOG (Out-of-Band to On-Ground) and IBTU (In-Band to Tethered-UAV) backhauling mode, and give the descriptions of the performance metrics for each mode. Then, the considered problem is formulated as an optimization of achievable rate. We derive the optimal solutions for the involved three backhauling modes for ASCs respectively, and closed-form optimal value for each mode is acquired with proof. We also give a pseudo-code form of our proposed optimal access/backhaul spectrum allocation algorithm. The simulation results indicate that the proposed scheme can deliver a significant gain, while IBTU performs best among proposed backhauling modes.

User-relay assisted cellular networks with multiple antennas

ABSTRACTUser-relay assisted OFDMA-based cellular networks have gained great importance recently since these networks are indicated as one of the powerful technologies that will contribute the 5G standard. These networks can be used with novel three-phase frame structure unlike classical two-phase frame structure and can be enhanced with multiple antennas to utilise the advantages of them. The main advantage of the three-phase frame structure is taking care of the limitations of the current transceiver design in practical systems and not allowing users to be relay and user simultaneously. Diversity and capacity gains are also the advantages of extending the network with multiple antennas. In this paper, we will use the novel three-phase frame structure for downlink MISO-OFDMA cellular networks and develop resource management algorithms as relay selection and resource allocation to observe the benefits of this system.

Poisson point process-based network modelling and performance analysis of multi-hop D2D chain relay formation in heterogeneous wireless network

This article presents stochastic geometry-based unified analytical framework to analyse the functionality of device-to-device (D2D) assisted cellular network in inband overlay mode. Independent marked Poisson point process (imPPP) is incorporated to probabilistically model the aggregate interference and SINR distribution of the randomly distributed nodes in the spatial domain. The proposed framework is applied to evaluate the overall network coverage probability improvement due to minimisation of congestion in the base station oriented core network by involving more devices in direct communication (through traffic offloading). Statistical analysis of slotted-synchronous and slotted-asynchronous traffic patterns in terms of throughput for varying transmission probability is also presented. From the application point of view, in the case of base station service interruption due to any natural calamity or power failure, the proposed multi-hop D2D chain relay formation helps to extend the cellular coverage in cell edges as well as in adjacent affected cell areas.

Economic-Robust Transmission Opportunity Auction for D2D Communications in Cognitive Mesh Assisted Cellular Networks

Device-to-device (D2D) communications can potentially alleviate cellular network congestion by utilizing local available links, and have attracted intensive attention recently. Cognitive radio (CR) allows users to opportunistically access unused licensed spectrums. It thus serves as a great candidate technology for D2D communications, but has not been widely employed in cellular networks due to hardware development limitations. In this paper, we propose a new architecture, called cognitive mesh assisted cellular network (CMCN), in which several secondary service providers (SSPs) deploy CR routers to facilitate D2D communications among wireless users. To address the competition among the SSPs, we further construct a secondary spectrum auction market. Although a few works have studied spectrum auctions, most of them are designed for single-hop communications, and it is usually not clear whom a winning user communicates with. Uncertain spectrum availability is not considered in previous schemes either. In this paper, we propose a transmission opportunity auction scheme, called TOA, which can address these problems. Extensive simulations are conducted to validate the efficiency of the CMCN architecture and that of the TOA scheme.

Downlink Analysis in Unmanned Aerial Vehicle (UAV) Assisted Cellular Networks With Clustered Users

The use of unmanned aerial vehicles (UAVs) operating as aerial base stations (BSs) has emerged as a promising solution especially in scenarios requiring rapid deployments (e.g., in the cases of crowded hotspots, sporting events, emergencies, and natural disasters) in order to assist the ground BSs. In this paper, an analytical framework is provided to analyze the signal-to-interference-plus-noise ratio (SINR) coverage probability of UAV assisted cellular networks with clustered user equipments (UEs). Locations of UAVs and ground BSs are modeled as Poison point processes, and UEs are assumed to be distributed according to a Poisson cluster process around the projections of UAVs on the ground. Initially, the complementary cumulative distribution function and probability density function of path losses for both UAV and ground BS tiers are derived. Subsequently, association probabilities with each tier are obtained. SINR coverage probability is derived for the entire network using tools from stochastic geometry. Finally, area spectral efficiency (ASE) of the entire network is determined, and SINR coverage probability expression for a more general model is presented by considering that UAVs are located at different heights. Via numerical results, we have shown that UAV height and path-loss exponents play important roles on the coverage performance. Moreover, coverage probability can be improved with smaller number of UAVs, while better ASE is achieved by employing more UAVs and having UEs more compactly clustered around the UAVs.

Channel and queue aware joint relay selection and resource allocation for MISO-OFDMA based user-relay assisted cellular networks

User-relay assisted orthogonal frequency division multiple access (OFDMA) networks are cost-effective solutions to meet the growing capacity and coverage demands of the next generation cellular networks. These networks can be used with multiple antennas technology in order to obtain a diversity gain to combat signal fading and to obtain more capacity gain without increasing the bandwidth or transmit power. Efficient relay selection and resource allocation are crucial in such a multi-user, multi-relay and multi-antenna environment to fully exploit the benefits of the combination of user-relaying and multiple antennas technology. Thus, we propose a channel and queue aware joint relay selection and resource allocation algorithm for multiple-input single-output (MISO)-OFDMA based user-relay assisted downlink cellular networks. Since, the proposed algorithm is not only channel but also queue-aware, the system resources are allocated efficiently among the users. The proposed algorithm for the MISO-OFDMA based user-relay assisted scheme is compared to existing MISO-OFDMA based non-relaying and fixed relay assisted schemes and it is also compared with the existing single-input single-output (SISO)-OFDMA based user-relay assisted scheme. Simulation results revealed that the proposed scheme outperforms the existing schemes in terms of cell-edge users’ total data rate, average backlog and average delay.

Relay Placement for Coverage Extension in Cellular Wireless Networks Under Composite Fading Model

In relay-assisted cellular networks, mobile stations are connected to base station through two or more single-hop communication links, where the intermediate nodes act as relay stations (RSs). The focus of this paper is on two-hop relay assisted cellular networks, where optimal relay placement is a crucial issue for achieving maximum extension of the cell coverage. However, the location of RS has significant impact on signal-to-interference plus noise ratio (i.e., SINR) and outage probability experienced on the access and backhaul links. Moreover, the frequency re-use factor also has significant influence on the SINR. In this paper, we develop analytical models for computing the SINR and outage probability performance of a two-hop relay assisted cellular network for both downlink (DL) as well as uplink (UL) transmission scenarios, considering the impact of path loss, shadowing, Nakagami fading and co-channel interference. We then investigate optimal placement of RS while satisfying the required criterion on probability of correct decoding, initially by considering the DL scenario alone and then by considering both DL and UL scenarios jointly. Through extensive evaluations, we report the impact of realistic propagation models on outage probability, optimal relay position and the cell coverage radius. Further, the model can be used to find the impact of co-channel re-use factor on optimal relay positioning in two-hop cellular networks.

User-Priority-Based Power Control Over the D2D Assisted Internet of Vehicles for Mobile Health

A device-to-device (D2D) assisted cellular network is pervasive to support ubiquitous healthcare applications, since it is expected to bring the significant benefits of improving user throughput, extending the battery life of mobiles, etc. However, D2D and cellular communications in the same network may cause cross-tier interference (CTI) to each other. Also a critical issue of using D2D assisted cellular networks under a healthcare scenario is the electromagnetic interference (EMI) caused by RF transmission, and a high level of EMI may lead to a critical malfunction of medical equipments. In consideration of CTI and EMI, we study the problem of optimizing individual channel rates of the mobile users in different priorities (different levels of emergency) within the Internet of Vehicles for mobile health, and propose an algorithm of controlling the transmit power to solve the above-mentioned problem under a game-theoretical framework. Numerical results show that the proposed algorithm can converge linearly to the optimum, while ensuring an allowable level of EMI on medical equipments.

User-Priority-Based Power Control in D2D Networks for Mobile Health

A device-to-device (D2D) assisted cellular network is pervasive to support ubiquitous healthcare applications, since it is expected to bring the significant benefits of improving user throughput, extending the battery life of mobiles, etc. However, D2D and cellular communications in the same network may cause cross-tier interference (CTI) to each other. Also a critical issue of using D2D assisted cellular networks under a healthcare scenario is the electromagnetic interference (EMI) caused by RF transmission, and a high level of EMI may lead to a critical malfunction of medical equipments. In consideration of CTI and EMI, we study the problem of optimizing the energy efficiency (EE) across the mobile users in different priorities (different levels of emergency) within the Internet of vehicles for mobile health, and propose a penalty-function algorithm of power control to solve the aforementioned problem. Numerical results demonstrate that the proposed algorithm can achieve remarkable improvements in terms of EE, while ensuring an allowable level of EMI on medical equipments.

Device-to-Device-Assisted Communications in Cellular Networks: An Energy Efficient Approach in Downlink Video Sharing Scenario

Cellular network is widely used and device-to-device (D2D)-assisted approaches have been proposed for improving performance on spectrum efficiency, overall throughput and energy efficiency. However, few of them have considered the downlink transmission for multiple concurrent devices from an energy efficiency perspective. In this paper, we focus on a D2D-assisted cellular communication in video stream sharing scenario. Two energy saving solutions for downlink transmission are proposed with constraint on D2D cluster’s energy consumption. We take peak signal-to-noise ratio (PSNR) as the measurement for video quality and consider both the downlink transmission energy and reception energy. In particular, we propose the D2D cluster formation approach and the D2D caching performance both for the purpose of energy saving, with distributed merge-and-split algorithm adopted from the perspective of coalition game theory and a relaxation factor defined to give constraints on total energy consumption for each cluster. Both D2D cluster and D2D caching approaches are effective for energy saving for the BS combined with all user devices; however, D2D cluster brings an unfairness problem between the cluster head and other cluster nodes. Therefore, we compare the two approaches on energy saving performance as well as fairness measurement. Moreover, a centralized algorithm for D2D cluster is also proposed as a benchmark for the distributed D2D cluster algorithm. Simulation result shows considerable amount of energy saving in the proposed D2D cluster and caching assisted cellular network for video stream sharing problem.